Earlier quoted context omitted.
> why is it that the LLM gives an answer when we can prove that it cannot answer this problem correctly? Brcause LLMs are not “problem solving machines” they are text completion models, so (when trained for q-and-a response) their function is to produce text output which forms a plausible seeming response to the question posed, not to execute an algorithm which solves the logical problem it communicatss. Asking “why…
> their function is to produce text output which forms a plausible seeming response to the question posed Answering "I don't know" or "I can't answer that" is a perfectly plausible response to a difficult logical problem/question. And it would not be a hallucination.
Hallucination is inevitable: An innate limitation of large language models
371–380 of 491 posts
Re: Hallucination is inevitable: An innate limitation of large language models
#372Earlier quoted context omitted.
Around 12k fatal outcomes have been reported in the EU after vaccination, but it is not certain in all cases that vaccines were the cause. The vaccine tracking chips come from two Microsoft (-affiliate) patents, one about using chips to track body activity to reward in cryptocurrency, and another about putting a vaccine passport chip in the hands of African immigrants. That vaccines contain tracking chips is a fabric…
Here out from the German wikipedia about the lockdown being used to cover up the use of children for their blood: "According to the initial interpretation, the mass quarantine (the "lockdown") does not serve to combat the pandemic, but is intended to provide Trump and his allies with an excuse to free countless children from torture chambers, where adrenochrome is being withdrawn en masse on behalf of the elite." – t…
Re: Hallucination is inevitable: An innate limitation of large language models
#373Earlier quoted context omitted.
No, I don't know how other people think but I just focus on something and the answer pops into my head. I generally only use a step by step process if I'm following steps given to me.
>but I just focus on something and the answer pops into my head. It's perfectly valid to say "I don't know", because no one really understand these parts of the human mind. The point here is saying "Oh the LLM thinks word by word, but I have a magical black box that just works" isn't good science, nor is it a good means of judging what LLMs are capable or not capable of.
Re: Hallucination is inevitable: An innate limitation of large language models
#374Earlier quoted context omitted.
> E.g. LLMs are reasonable at chess and turns out somewhere in the blob there’s a chessboard representation, and you can make the model believe the board is in a different state by tweaking those parameters. Broadly agreed, but there's no "representation"...the model has no memory, let alone a "concept" of a chessboard. It's just trained on a bunch of textual replays of chess games, and this works well enough for a g…
The fact that tweaking parameters which appear to store the board makes it play according to the tweaked numbers instead of what was passed to it the context (i.e. working memory) directly contradicts your assertion that LLMs have no memory. The context is their memory. I can’t comment on your drug generation task - they aren’t magic, if the training didn’t result in a working drug model in the billions of params you…
> My bet is on learned world models because I’m not convinced there’s magic in our physical world.
You don't need to bet, and it has nothing to do with "magic". They quite literally have no ability to have a "world model" -- it's just a text generator, producing tokens. There's no working set memory, other than the text you pass into it. It should hopefully be obvious to you that when you write, you're not simply emitting one word at a time. You have a complete mental model of whatever you're discussing, stored in working memory, and it's persistent. We also update that model with every interaction we have.
The point of my post was that as soon as you take on a harder problem than simulating language, the lack of intelligence slaps you in the face. It turns out that understandable, coherent free-text responses is not magic, and the surprising result is that human language is regular enough that you can statistically simulate "intelligence" with a few hundred million free parameters.
Re: Hallucination is inevitable: An innate limitation of large language models
#375Earlier quoted context omitted.
Sure. I think LLMs are good at that kind of thing. My final example demonstrates how those cultural norms cause errors, it was from a logical thinking session at university, where none of the rest of my group could accept my (correct) claim that the answer was "not enough information to answer" even when I gave a (different but also plausible) non-robbery scenario and pointed out that we were in a logical thinking tr…
Don't you think it's strange that humans have little to no interest when root causes to their problems are found?
No idea what you're getting at here, though.
Re: Hallucination is inevitable: An innate limitation of large language models
#376Earlier quoted context omitted.
> Sure, and you can train LLMs to produce answers like that more often, but then users will say your model is lazy and doesn't even try, whereas if you train it to be more likely to produce something that looks like a solution more often, people will think “wow, the AI solved this problem I couldn't solve”. Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know"…
>Are you saying that LLMs can't learn to discriminate between which questions they should answer "I don't know" vs which questions they should try to provide an accurate answer? This is highly problematic and highly contextualized statement. Imagine you're an accountant with the piece of information $x. The answer you give for the statement "What is $x" is going to be highly dependent on who is answering the question…
I assume you meant asking rather than answering?
> An LLM doesn't have the other human motivations a person does when asked questions, pretty much at this point with LLMs there are only one or two 'voices' it hears (system prompt and user messages).
Why would LLMs need any motivation besides how they are trained to be helpful and the given prompts? In my experience with ChatGPT 4, it seems to be pretty good at discerning what and how to answer based on the prompts and context alone.
> Whereas a human will commonly lie and say I don't know, it's somewhat questionable if we want LLMs intentionally lying.
Why did you jump to the conclusion that an LLM answering "I don't know" is lying?
I want LLMs to answer "I don't know" when they don't have enough information to provide a true answer. That's not lying, in fact it's the opposite, because the alternative is to hallucinate an answer. Hallucinations are the "lies" in this scenario.
> In addition human information is quite often compartmentalized to keep secrets which is currently not in vogue with LLMs as we are attempting to make oracles that know everything with them.
I'd rather have an oracle that can discriminate when it doesn't have enough information to provide a true answer and replies "I don't know" in such cases (or sometimes answer like "If I were to guess, then bla bla bla, but I'm not sure about this"), than one which always gives confident but sometimes wrong answers.
Re: Hallucination is inevitable: An innate limitation of large language models
#377Earlier quoted context omitted.
Hallucination is a misnomer in LLMs and it depresses me that it has solidified as terminology. When humans do this, we call it confabulation. This is a psychiatric symptom where the sufferer can't tell that they're lying, but fills in the gaps in their knowledge with bullshit which they make up on the spot. Hallucination is an entirely different symptom. And no, confabulation isn't a normal thing which humans do, and…
>confabulation isn't a normal thing which humans do > A normal person is aware of the limits of their knowledge, for whatever reason, LLMs are not. Eh, both of these things are far more complicated. People perform minor confabulations all the time. Now, there is a medical term for confabulation to about a more serious medical condition that involves high rates of this occurring coupled with dementia, and would be the…
So I'm in the camp where LMMs are confabulating - and there's and I personally think the argument that they can be seen as confabulation machines has some validity.
Re: Hallucination is inevitable: An innate limitation of large language models
#378Earlier quoted context omitted.
The models that exist now say "I don't know" all the time. It's so weird that people keep insisting that it can't do things that it does. Ask it what dark matter is, and it won't invent an answer, it will present existing theories and say that it's unknown. Ask it about a person you know that isn't in it's data set and it'll tell you it has no information about the person. Despite the fact that people insist that hal…
I have to use LLMs for work projects - which are not PoCs. I can’t have a tool that makes up stuff an unknown amount of time. There is a world of research examining hallucination Rates, indicating hallucination rates of 30%+. With steps to reduce it using RAGs, you could potentially improve the results significantly - last I checked it was 80-90%. And the failure types aren’t just accuracy, it’s precision, recall, re…
I want to see a citation for this. And a clear definition for what is a hallucination and what isn't.
Re: Hallucination is inevitable: An innate limitation of large language models
#379Earlier quoted context omitted.
It's statistical prediction. LLMs do not "understand" the world by definition. Ask an image generator to make "an image of a woman sitting on a bus and reading a book". Images will be either a horror show or at best full of weird details that do not match the real world - because it's not how any of this works. It's a glorified auto-complete that only works due to the massive amounts of data it is trained on. Throw i…
Why do people say stuff like this that is so demonstrably untrue? SD and GPT4 do not exhibit the behavior described above and they're not even new.
it's true that most people do not actually understand the problem/limitation, but it's a discussion that is statistically likely to occur on the internet and therefore people tend to regurgitate the words without understanding the concept.
I'm being facetious but honestly it's a major theme of this whole AI revolution, people do not want to accept that humans are just another kind of machine and that their own cognition resembles AI/ML in virtually every aspect. People confabulate. People overreach the bounds of their expertise. People repeat words and concepts without properly understanding the larger context in which they need to be applied. Etc etc.
Has nobody ever watched someone get asked a big question or an unexpected question and "watched the wheels turn", or watched them stammer out some slop of incoherent words while they're processing? Does nobody have "canned responses" that summarize a topic that you can give pretty much the same (but not exactly, of course) every time you are asked it? Is that not "stochastic word chains"?
By design neural nets work almost exactly the same as your brain. But a lot of people are trapped in the idea that there must be some kind of "soul" or something that makes human cognition fundamentally different. By design, it's not. And we don't fully understand the exact modalities to encode information in it usefully and process it yet, but that's what the whole process here is about.
(I commented about this maybe 6 months ago, but the real hot take is that what we think of as "consciousness" isn't a real thing, or even an "overseer" within the mind - "consciousness" may be exactly the thing people mean when they say that "LLMs have to write a word every time they think about a concept". "Consciousness" may in fact be a low-dimensional projection of the actual computation occurring in the brain itself, rationalizing and explicating the symbolic computations of the brain in some form that can be written down and communicated to other humans. "Language" and "consciousness" as top-level concepts may actually only be an annex that our brain has built for storing and communicating those symbolic computations, rather than a primary driver of the computations itself. It's not in control, it's only explaining decisions that we already have made... we see the shadows on the wall of plato's cave and think that's the entire world, but it's really only a low-dimensional projection.)
(or in other words - everyone assumes consciousness is the OS, or at least the application. But consciousness may actually be the json serializer/deserializer - ie not actually the thing in control at all. Our entire lives and decisionmaking processes may in fact be simple rationalizations and explanations around "what the subconscious mind thinks should happen next".)
Re: Hallucination is inevitable: An innate limitation of large language models
#380Earlier quoted context omitted.
Why do people say stuff like this that is so demonstrably untrue? SD and GPT4 do not exhibit the behavior described above and they're not even new.
Neither of these comments are accurate. (edit: but renegade-otter is more correct) Here's 1.5 EMA https://imgur.com/mJPKuIb Here's 2.0 EMA https://imgur.com/KrPVUGy No negatives, no nothing just the prompt. 20 steps of DPM++ 2M Karras, CFG of 7, seed is 1. Can we make it better? Yeah sure, here's some examples: https://imgur.com/Dmx78xV , https://imgur.com/HBTitWm But I changed the prompt and switched to DPM++ 3M SDE…
Honestly these pictures you posted do prove GP's point...